3,728 research outputs found
DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation
In recent years, there has been growing focus on the study of automated
recommender systems. Music recommendation systems serve as a prominent domain
for such works, both from an academic and a commercial perspective. A
fundamental aspect of music perception is that music is experienced in temporal
context and in sequence. In this work we present DJ-MC, a novel
reinforcement-learning framework for music recommendation that does not
recommend songs individually but rather song sequences, or playlists, based on
a model of preferences for both songs and song transitions. The model is
learned online and is uniquely adapted for each listener. To reduce exploration
time, DJ-MC exploits user feedback to initialize a model, which it subsequently
updates by reinforcement. We evaluate our framework with human participants
using both real song and playlist data. Our results indicate that DJ-MC's
ability to recommend sequences of songs provides a significant improvement over
more straightforward approaches, which do not take transitions into account.Comment: -Updated to the most recent and completed version (to be presented at
AAMAS 2015) -Updated author list. in Autonomous Agents and Multiagent Systems
(AAMAS) 2015, Istanbul, Turkey, May 201
Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste
Recommender systems are increasingly used to predict and serve content that
aligns with user taste, yet the task of matching new users with relevant
content remains a challenge. We consider podcasting to be an emerging medium
with rapid growth in adoption, and discuss challenges that arise when applying
traditional recommendation approaches to address the cold-start problem. Using
music consumption behavior, we examine two main techniques in inferring Spotify
users preferences over more than 200k podcasts. Our results show significant
improvements in consumption of up to 50\% for both offline and online
experiments. We provide extensive analysis on model performance and examine the
degree to which music data as an input source introduces bias in
recommendations.Comment: SIGIR 202
Explainability in Music Recommender Systems
The most common way to listen to recorded music nowadays is via streaming
platforms which provide access to tens of millions of tracks. To assist users
in effectively browsing these large catalogs, the integration of Music
Recommender Systems (MRSs) has become essential. Current real-world MRSs are
often quite complex and optimized for recommendation accuracy. They combine
several building blocks based on collaborative filtering and content-based
recommendation. This complexity can hinder the ability to explain
recommendations to end users, which is particularly important for
recommendations perceived as unexpected or inappropriate. While pure
recommendation performance often correlates with user satisfaction,
explainability has a positive impact on other factors such as trust and
forgiveness, which are ultimately essential to maintain user loyalty.
In this article, we discuss how explainability can be addressed in the
context of MRSs. We provide perspectives on how explainability could improve
music recommendation algorithms and enhance user experience. First, we review
common dimensions and goals of recommenders' explainability and in general of
eXplainable Artificial Intelligence (XAI), and elaborate on the extent to which
these apply -- or need to be adapted -- to the specific characteristics of
music consumption and recommendation. Then, we show how explainability
components can be integrated within a MRS and in what form explanations can be
provided. Since the evaluation of explanation quality is decoupled from pure
accuracy-based evaluation criteria, we also discuss requirements and strategies
for evaluating explanations of music recommendations. Finally, we describe the
current challenges for introducing explainability within a large-scale
industrial music recommender system and provide research perspectives.Comment: To appear in AI Magazine, Special Topic on Recommender Systems 202
Support the Underground: Characteristics of Beyond-Mainstream Music Listeners
Music recommender systems have become an integral part of music streaming
services such as Spotify and Last.fm to assist users navigating the extensive
music collections offered by them. However, while music listeners interested in
mainstream music are traditionally served well by music recommender systems,
users interested in music beyond the mainstream (i.e., non-popular music)
rarely receive relevant recommendations. In this paper, we study the
characteristics of beyond-mainstream music and music listeners and analyze to
what extent these characteristics impact the quality of music recommendations
provided. Therefore, we create a novel dataset consisting of Last.fm listening
histories of several thousand beyond-mainstream music listeners, which we
enrich with additional metadata describing music tracks and music listeners.
Our analysis of this dataset shows four subgroups within the group of
beyond-mainstream music listeners that differ not only with respect to their
preferred music but also with their demographic characteristics. Furthermore,
we evaluate the quality of music recommendations that these subgroups are
provided with four different recommendation algorithms where we find
significant differences between the groups. Specifically, our results show a
positive correlation between a subgroup's openness towards music listened to by
members of other subgroups and recommendation accuracy. We believe that our
findings provide valuable insights for developing improved user models and
recommendation approaches to better serve beyond-mainstream music listeners.Comment: Accepted for publication in EPJ Data Science - link to published
version will be adde
The Multisided Complexity of Fairness in Recommender Systems
Recommender systems are poised at the interface between stakeholders: for example, job applicants and employers in the case of recommendations of employment listings, or artists and listeners in the case of music recommendation. In such multisided platforms, recommender systems play a key role in enabling discovery of products and information at large scales. However, as they have become more and more pervasive in society, the equitable distribution of their benefits and harms have been increasingly under scrutiny, as is the case with machine learning generally. While recommender systems can exhibit many of the biases encountered in other machine learning settings, the intersection of personalization and multisidedness makes the question of fairness in recommender systems manifest itself quite differently. In this article, we discuss recent work in the area of multisided fairness in recommendation, starting with a brief introduction to core ideas in algorithmic fairness and multistakeholder recommendation. We describe techniques for measuring fairness and algorithmic approaches for enhancing fairness in recommendation outputs. We also discuss feedback and popularity effects that can lead to unfair recommendation outcomes. Finally, we introduce several promising directions for future research in this area
Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models
Modern recommender systems lie at the heart of complex ecosystems that couple
the behavior of users, content providers, advertisers, and other actors.
Despite this, the focus of the majority of recommender research -- and most
practical recommenders of any import -- is on the local, myopic optimization of
the recommendations made to individual users. This comes at a significant cost
to the long-term utility that recommenders could generate for its users. We
argue that explicitly modeling the incentives and behaviors of all actors in
the system -- and the interactions among them induced by the recommender's
policy -- is strictly necessary if one is to maximize the value the system
brings to these actors and improve overall ecosystem "health". Doing so
requires: optimization over long horizons using techniques such as
reinforcement learning; making inevitable tradeoffs in the utility that can be
generated for different actors using the methods of social choice; reducing
information asymmetry, while accounting for incentives and strategic behavior,
using the tools of mechanism design; better modeling of both user and
item-provider behaviors by incorporating notions from behavioral economics and
psychology; and exploiting recent advances in generative and foundation models
to make these mechanisms interpretable and actionable. We propose a conceptual
framework that encompasses these elements, and articulate a number of research
challenges that emerge at the intersection of these different disciplines
Towards Responsible Media Recommendation
Reading or viewing recommendations are a common feature on modern media sites. What is shown to consumers as recommendations is nowadays often automatically determined by AI algorithms, typically with the goal of helping consumers discover relevant content more easily. However, the highlighting or filtering of information that comes with such recommendations may lead to undesired effects on consumers or even society, for example, when an algorithm leads to the creation of filter bubbles or amplifies the spread of misinformation. These well-documented phenomena create a need for improved mechanisms for responsible media recommendation, which avoid such negative effects of recommender systems. In this research note, we review the threats and challenges that may result from the use of automated media recommendation technology, and we outline possible steps to mitigate such undesired societal effects in the future.publishedVersio
Pay-to-Playlist: The Commerce of Music Streaming
Payola—sometimes referred to as “pay-for-play”—is the undisclosed payment, or acceptance of payment, in cash or in kind, for promotion of a song, album, or artist. Some form of pay-for-play has existed in the music industry since the nineteenth century. Most prominently, the term has been used to refer to the practice of musicians and record labels paying radio DJs to play certain songs in order to boost their popularity and sales. Since the middle of the twentieth century, the FCC has regulated this behavior—ostensibly because of its propensity to harm consumers and competition—by requiring that broadcasters disclose such payments.
As streaming music platforms continue to siphon off listeners from analog radio, a new form of payola has emerged. In this new streaming payola, musicians and labels simply shift their payments from radio to streaming music platforms like Spotify, YouTube, TikTok, and Instagram. Instead of going to DJs, payments (or their equivalents) go to platforms, third-party playlisters, and influencers who can help promote a song by directing audiences toward it. Because online platforms do not fall under the Federal Communications Commission’s (FCC’s) jurisdiction, streaming pay-for-play is not currently regulated at the federal level, although some of it may be subject to state advertising disclosure laws.
In this Article, we describe the history and regulation of traditional forms of pay-for-play and explain how streaming payola practices differ. Our account is based, in substantive part, on a novel series of qualitative interviews with music industry professionals. Our analysis finds the normative case for regulating the most common form of streaming payola lacking: contrary to conventional wisdom, we show that streaming pay-for-play paid to third parties, whether disclosed or not, likely causes little to no harm to consumers and may even help independent artists gain access to a broader audience. The case of “reverse payola,” in which a platform itself offers promotion in exchange for paying out a lower-than-market royalty rate, is potentially more concerning. Given this state of affairs, regulators should proceed with caution to preserve the potential advantages afforded by streaming payola while avoiding further exacerbating extant inequalities and anticompetitive concerns in the music industry
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